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Datarails operations on the OPEN Excel workbook. Use this whenever — in Excel — the user wants to refresh / recalculate / update the Datarails numbers, drill down or break down a cell, add or insert a DR.GET function, create a dynamic range, evaluate or read what a DR.GET cell returns, publish to a dashboard, connect the workbook, or submit. Runs through the Datarails Excel Add-In via the hidden __dr_agent bridge sheet — this is "the agent". NOT the datarails-finance-os MCP connector — the connector answers org-data questions (list tables/models/fields, aggregations, distinct values, metrics, org users, FX) and stays correct even while a workbook is open; use THIS skill only to act on the open workbook. Trigger phrases: "refresh", "recalculate", "update the numbers", "drill into this", "what's behind this cell", "add a function", "DR.GET", "publish", "submit".

Detect data anomalies in one Datarails Finance OS table and answer IN CHAT — severity-ranked outliers, duplicates, missing/null rates, rare values — scoped to the latest complete fiscal year. Writes no file (use the anomalies-report skill for an Excel workbook; use the profile skill for unscoped whole-history statistics). The MCP profiling and aggregation tools return baseline aggregates only (raw rows come from the separate get_data_by_* calls); this skill computes the findings client-side.

Detect data anomalies and generate a comprehensive data-quality Excel WORKBOOK from Finance OS tables, computed over the table's ALL-TIME history (use the anomalies skill for a chat-only answer scoped to the latest fiscal year — the two baselines differ by design, so counts won't match). The MCP tools return baseline aggregates only; this skill derives findings, severity buckets, and the Data Quality Score client-side, then writes a multi-sheet workbook. Self-contained — pass --table-id to target a table directly, or it discovers the financials table on its own; no profile or setup step required.

Generate an audit-support evidence package over FinanceOS data - completeness, reconciliation, mapping-integrity, and substantive-sample checks with a PDF report and Excel evidence workbook. Not a SOX certification - access-control, change-management, and IT-general-control evidence is out of scope.

Executive KPI dashboard — a ONE-MONTH metrics snapshot for the latest closed month (not real-time; the in-progress month is excluded) — Excel dashboard + single-slide PowerPoint one-pager. For a full-fiscal-year narrative deck use insights; for a year-long analysis workbook use intelligence; for a raw data export use extract.

Analyze P&L and performance by department. Creates departmental reports and comparative analysis with Excel and PowerPoint outputs.

Drill down on a cell in the Datarails Excel Add-in to see underlying detail — also the skill to use when the user asks to "explain the variance", "explain this number", "what's driving this", "what's behind this cell", or break a figure into its line items. In a live Excel context (add-in agent bridge available) DR formula cells drill through the add-in's own drill-down; with an .xlsx file (Claude Code) it resolves DR.GET formulas, reads hidden "dr control" filters, queries Datarails, and validates totals; with no workbook at all, the user can paste a DR.GET formula or give structured filters (no-file mode). Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Top expense categories, monthly expense trends, and concentration analysis from real aggregated data. Self-contained — discovers the client's financials table and fields itself, no profile or setup step required.

Extract validated financial data from Datarails Finance OS to Excel — a RAW FULL-YEAR export — a 4-sheet workbook (P&L, Balance Sheet, KPIs including SaaS metrics where sourceable from the org's data, and validation checks); no analysis or narrative. For an analyzed workbook use intelligence; for an executive deck use insights. Self-contained — discovers the client's tables and fields on its own, no profile or setup step required.

Quick snapshot of revenue, expenses, gross profit, and margin from real aggregated totals. Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Analyze budget vs forecast vs actual variances. Compares multi-scenario financial data for planning and performance review.

Generate Excel workbooks with DR.GET formulas that pull live financial data from Datarails. Creates P&L templates, budget models, and variance reports with validated dimension values. Asks which account level to cut the P&L at and which design to use before building. Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Executive-ready insights with trend analysis and visualizations — a FULL-FISCAL-YEAR narrative deck — multi-slide PowerPoint presentation plus a supporting Excel data book. For a one-month KPI snapshot use dashboard; for a 10-sheet analysis workbook (no PowerPoint) use intelligence.

Comprehensive FP&A financial-analysis intelligence workbook for a FULL FISCAL YEAR — Excel, up to 10 sheets, with ranked auto-detected insights, findings and recommendations, and professional formatting; no PowerPoint. For a one-month KPI snapshot use dashboard; for an executive deck use insights. Self-contained — discovers the client's tables and fields on its own, no profile or setup step required.

Profile Datarails Finance OS table fields — how is a field distributed? Per-field statistics over the table's whole history — ranges, approximate percentiles, null rates, cardinality interpretation, and range-outlier flags — no severity ranking, no period scope. The MCP tools return baseline aggregates (SUM/AVG/MIN/MAX/COUNT for numeric, distinct-value samples for categorical); this skill derives the statistics client-side. For severity-ranked data-quality findings scoped to the latest fiscal year, use the anomalies skill.

Query Datarails Finance OS tables with filters. Fetch specific records or page through rows for investigation. The filter API supports value-list AND advanced operators — comparisons, ranges, text matching, null checks, and date ranges all work.

Whole-period consistency WORKBOOK - run independent-source checks across Finance OS data (cross-endpoint agreement, balance-sheet identity, cross-grain roll-ups, scenario/period integrity). Validates the data pipeline and mappings, not source systems. NOT a per-metric layer comparison - that is the dev-only cross-layer-reconcile harness.

Revenue trends, growth rates, and composition from real aggregated monthly data. Self-contained — discovers the client's financials table and fields itself, no profile or setup step required.

List and explore Datarails Finance OS tables — discover what data is available and view one table's SCHEMA (fields, types, distinct values of a field) to understand its structure. For per-field STATISTICS (ranges, percentiles, null rates, cardinality) use the profile skill. Works with or without an open workbook — in Excel, "list my Datarails tables/models/fields" still routes HERE via the MCP connector (the Excel bridge's agent.list_functions lists workbook widgets, not org tables).

Test API field compatibility and performance. Discovers which fields work with aggregation and suggests sibling alternatives for failed fields, then reports the results. Self-contained — discovers the client's financials table and candidate fields on its own, no profile or setup step required.